Over the past few years, intravascular ultrasound (IVUS) technology has become very useful for studying atherosclerotic disease. IVUS is a medical imaging technique that produces cross-sectional images as a catheter is pulled-back inside a blood vessel. These images show the lumen but also the layered structure of the vascular wall. IVUS provides quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as the plaque shape and size such that in clinic, IVUS was rapidly recognized as a valuable tool in diagnosis and in pre-intervention analysis of atherosclerosis.
The ability to characterize the vascular wall was initially demonstrated in 1989 by Gussenhoven et al., in “Arterial wall characteristics determined by intravascular ultrasound imaging: An in vitro study” (J. Am. Coll. Cardiol., vol. 14, no. 4, pp. 947-952, 1989). Also, studies of the mid-90s by Mintz et al., in “Atherosclerosis in angiographically ‘normal’ coronary artery reference segments: An intravascular ultrasound study with clinical correlations” (J. Am. Coll. Cardiol., vol. 25, no. 7, pp. 1479-1485, 1995), showed, based on IVUS, that 40% of angiographically normal vessel were in fact atherosclerotic.
By using IVUS, it was also demonstrated by Colombo et al., in “Intracoronary stenting without anticoagulation accomplished with intravascular ultrasound guidance” (Circulation, vol. 91, pp. 1676-1688, 1995) that conventional stent implantation resulted in incomplete apposition and expansion causing thrombosis, which had the result of changing the clinical practice.
IVUS is also expected to play an important role in atherosclerosis research. For example, as demonstrated by Nissen et al., in “Application of intravascular ultrasound to characterize coronary artery disease and assess the progression or regression of atherosclerosis” (Am. J. Cardiol., vol. 89, pp. 24B-31B, 2002), IVUS helps to achieve precise evaluation of the disease in new progression-regression therapies. Experts also generally agree that IVUS imaging adds precious complementary information to angiography which only shows a projection of the lumen, as taught by Nissen et al., in “Intravascular ultrasound: Novel pathophysiological insights and current clinical applications” (Circulation, vol. 103, pp. 604-616, 2001).
Over the last few years, new signal processing strategies were applied to IVUS signals for extracting information on the elastic properties of the vascular wall. For example, a new imaging technique named “intravascular or endovascular ultrasound elastography” was proposed by de Korte et al., in “Intravascular elasticity imaging using ultrasound—Feasibility studies in phantoms” (Ultrasound Med. Bio., vol. 23, pp. 735-746, 1997). Recently, Brusseau et al. in “Fully automatic luminal contour segmentation in intracoronary ultrasound imaging—A statistical approach” (IEEE Trans. Med. Imag., vol. 23, pp. 554-566, 2004) suggested to use a pre-segmentation of the structures of the vascular wall identified from IVUS images to help implementing elastography algorithms. This constitutes another important domain of application of IVUS multi-dimensional image segmentation.
The tomographic nature of IVUS makes 3D reconstruction of the vessel wall possible. Furthermore, 2D and 3D quantitative measurements of atherosclerotic disease such as plaque volume, intima-media thickness, vascular remodeling, and lumen area stenosis can be retrieved from IVUS data as disclosed by Mintz et al., in “American college of cardiology clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound studies (IVUS)” (J. Am. Coll. Cardiol, vol. 37, no. 5, pp. 1478-1492, 2001).
However, a typical IVUS acquisition generally contains several hundreds of images, which has the effect of making analysis of the data a long and fastidious task that is further subject to an important variability between intra-observers and inter-observers when non-automatic methods are used. These aspects generate important constraints against the clinical use of IVUS. Other constraints related to the use of IVUS include poor quality image due to speckle noise, imaging artifacts, and shadowing of parts of the vessel wall by calcifications.
So far, a number of segmentation techniques have been developed for IVUS data analysis and were introduced to overcome the hereinabove discussed drawbacks. Generally speaking, a portion of these techniques are based on local properties of image pixels, namely with the gradient-based active surfaces as introduced by Klingensmith et al., in “Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images” (IEEE Trans. Med. Imag., vol. 19, no. 10, pp. 996-1011, 2000) and the pixel intensity combined to gradient active contours as introduced by Kovalski et al., in “Three-dimensional automatic quantitative analysis of intravascular ultrasound images” (Ultrasound Med. Biol., vol. 26, no. 4, pp. 527-537, 2000).
Graph search was also investigated using local pixel features, for instance, with Sobel-like edge operator as disclosed by Zhang et al., in “Tissue characterization in intravascular ultrasound images” (IEEE Trans. Med. Imag., vol. 17, no. 6, pp. 889-899, 1998) and with gradient associated to line patterns correlation as demonstrated by Von Birgelen et al., in “Morphometric analysis in three-dimensional intracoronary ultrasound: An in vitro and in vivo study using a novel system for the contour detection of lumen and plaque” (Am. Heart J., vol. 132, no. 2, pp. 516-527, 1996).
The other portion of the IVUS segmentation work was based on more global or region information. For instance, texture-based morphological processing was considered as disclosed by Mojsilovic et al., in “Automatic segmentation of intravascular ultrasound images: A texture-based approach” (Ann. Biomed. Eng., vol. 25, no. 6, pp. 1059-1071, 1997). Gray level variances were then used for the optimization of a maximum a posteriori (MAP) estimator modeling ultrasound speckle and contour geometry as demonstrated by Haas et al., in “Segmentation of 3D intravascular ultrasonic images based on a random field model” (Ultrasound Med. Biol., vol. 26, no. 2, pp. 297-306, 2000).
In addition, some studies defining only the lumen boundary and not using the full IVUS potential can be found in the literature. Still, in 2001, the clinical expert consensus from the American College of Cardiology in the hereinabove cited document by Mintz et al. reported that no IVUS edge detection method had found widespread acceptance by clinicians.
Recently, graph search was revisited using other edge filters, as disclosed by Koning et al., in “Advanced contour detection for three-dimensional intracoronary ultrasound: A validation—in vitro and in vivo” (Int. J. Cardiac Imag., vol. 18, pp. 235-248, 2002).
Other recent models and methods were proposed, such as elliptical template fitting as demonstrated by Weichert et al., in “Virtual 3D IVUS model for intravascular brachytherapy planning: 3D segmentation, reconstruction, and visualization of coronary artery architecture and orientation” (Med. Phys., vol. 30, no. 9, pp. 2530-2536, 2003) and multiagent segmentation by Bovenkamp et al., in “Multiagent IVUS image interpretation” (SPIE Proceedings: Medical Imaging 2003: Image Processing, vol. 5032, San-Diego, USA, 2003, pp. 619-630). However, these new models were again using local pixel or edge information and they were not taking advantage of the statistical information of IVUS data (speckle texture).
Since image pixels in IVUS have pixel gray values generally distributed according to a Rayleigh probability density function (PDF) in B-mode (brightness modulation) imaging of uniform scattering tissues, as demonstrated by Wagner et al., in “Statistics of speckle in ultrasound B-scans” (IEEE Trans. Son. Ultrason., vol. 30, no. 3, pp. 156-163, 1983), it is believed that PDF features can be of value for IVUS segmentation. This information is hypothetically more suitable for IVUS image analysis, especially when contrast is low between layers of the vascular wall. In addition, because the IVUS radio-frequency (RF) mode generally provides a better spatial resolution than B-mode imaging, it is also expected that the Gaussian PDF of RF images can be exploited for image segmentation.
Since the atherosclerotic plaque structure on the vascular wall can have an irregular and complex shape that is rarely elliptical, a fast marching method as disclosed by Sethian in “Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluids Mechanics, Computer Vision and Materials Science” (2nd ed. Cambridge, UK: Cambridge University press, 1999) and by Osher et al., in “Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations” (J. Comput. Phys., vol. 79, pp. 1249, 1988), can be used to handle topological changes and contour irregularities generated by IVUS images. Further, the fact that a fast marching method propagates interfaces in the direction of the boundaries through an exhaustive analysis of the propagation region has the effect of decreasing the variability of segmentation results.